Although the literature is fulfilled with methods that employ image reconstruction for specific tasks, such as super-resolution image reconstruction [1]}, [2]}, tomographies [3]}, and denoising and debluring [4]}, including RBM [5]} and DBM [6]} approaches, data reconstruction in the context of this paper stands for RBM-based models as an intrinsic process of the learning step, whose error is monitored for optimization purpose [7]}, instead of a practical application itself. In other words, such models try to represent the input data in the hidden layers given their probability distribution. Such representation is supposedly capable of reconstructing a similar input given a stochastic Gibbs sampling. Afterward, the representation mentioned above is employed for a vast range of applications, such as classification [8]}, dimensionality reduction [9]}, modeling human motion [10]}, among others.
